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| 시계열 변분 추론× | 순차 몬테카를로 (Sequential Monte Carlo, SMC)× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 1999–2017 | 1993 (particle filter); 2006 (SMC samplers) |
| 창시자≠ | Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| 유형≠ | Approximate Bayesian inference | Sequential Bayesian computation |
| 원전≠ | Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877. DOI ↗ | Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113. DOI ↗ |
| 별칭 | time-series VI, variational Bayes for time series, TSVI, sequential variational inference | SMC, particle filter, sequential importance resampling, SMC sampler |
| 관련 | 6 | 6 |
| 요약≠ | Time series variational inference applies variational Bayes to sequential data, approximating the intractable posterior over latent states and parameters with a tractable family of distributions. By maximising the evidence lower bound (ELBO), it delivers fast, scalable Bayesian inference for state-space models, dynamic latent variable models, and other time-ordered probabilistic systems. | Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions. |
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